{"id":"W3020551119","doi":"10.1101/2020.04.24.20074195","title":"AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Bristol-Myers Squibb; U.S. Department of Defense; Eli Lilly and Company; BrightFocus Foundation; Novartis Pharmaceuticals Corporation; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Neuroimaging; Transfer of learning; Deep learning; Artificial intelligence; Computer science; Machine learning; Artificial neural network; Cognition; Intervention (counseling); Psychology; Neuroscience; Psychiatry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013551,0.001552491,0.0008076687,0.0009392513,0.00035814,0.0006745671,0.001495922,0.001164274,0.001967963],"category_scores_gemma":[0.002643136,0.000283041,0.0007135147,0.0005045809,0.0003071691,0.001064988,0.0009104346,0.00145749,0.0006034436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009136151,"about_ca_system_score_gemma":0.001084406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01064306,"about_ca_topic_score_gemma":0.01044276,"domain_scores_codex":[0.9997366,0.00005018541,0.00001881737,0.0000959325,0.00005382948,0.00004459302],"domain_scores_gemma":[0.9995865,0.0001340526,0.00004262051,0.00004857593,0.0001467108,0.00004151216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001435714,0.001040797,0.02569811,0.0002682724,0.0004473613,0.0004872104,0.00008798256,0.2444975,0.005980304,0.00222062,0.03328937,0.6845468],"study_design_scores_gemma":[0.00005970565,0.0001896917,0.003251374,0.00003817799,0.00008202914,0.0001384071,0.0000205155,0.9872728,0.003444653,0.003157557,0.002317773,0.00002725374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4429574,0.0158082,0.5004265,0.003163023,0.002306736,0.0005793002,0.006290311,0.01495187,0.01351675],"genre_scores_gemma":[0.913483,0.001463486,0.07001209,0.0007649727,0.0003090838,0.0002278957,0.004824982,0.0001293598,0.008785123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01064306,"threshold_uncertainty_score":0.02116221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04051736940011381,"score_gpt":0.317525302758097,"score_spread":0.2770079333579832,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}